import pandas as pd
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori, association_rules

df = pd.read_excel('./asd/餐厅数据.xlsx')
# print(df.head())

# 转换菜品格式
trsations = df['菜品'].str.split(',').to_list()

# 标准化
te = TransactionEncoder()
te_ary = te.fit(trsations).transform(trsations)
de_encoded = pd.DataFrame(te_ary, columns=te.columns_)

# 使用apriori进行分析
frequent_itemsets = apriori(de_encoded, min_support=0.1, use_colnames=True)
frequent_itemsets.sort_values(by='support', ascending=False, inplace=True)

# 选择二项集查看
print(frequent_itemsets[frequent_itemsets.itemsets.apply(lambda x: len(x) == 2)])

# 生成关联规则
rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=0.1)

# 筛选有效规则
effective = rules[
    (rules['lift'] > 1) & (rules['confidence'] > 0.1)
].sort_values(by=['lift', 'confidence'], ascending=False)

# print(effective[["antecedents", "consequents", "support", "confidence", "lift"]])

# 关联规则可视化
import matplotlib.pyplot as plt
import networkx as nx # type: ignore

# 设置字体
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

G = nx.DiGraph()
for _, row in effective.iterrows():
    G.add_edge(','.join(list(row['antecedents'])),
               ','.join(list(row['consequents'])),
               weight=row['lift'])

plt.figure(figsize=(12, 8))
pos = nx.spring_layout(G)
nx.draw(G, pos,
        with_labels=True,
        edge_color=[G[u][v]['weight'] for u,v in G.edges()],
        width=2.0,
        edge_cmap=plt.cm.Blues)
plt.show()